Systems and methods for monitoring pain management

ABSTRACT

The present disclosure relates to systems and methods for monitoring pain management using measurements of physiological parameters based on a PPG signal. A reference physiological parameter may be compared against a later measurement to identify a change in condition that may indicate a pain management problem.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/165,357, filed Mar. 31, 2009, and is incorporated by reference hereinin its entirety.

SUMMARY

The present disclosure relates to pain management monitoring using oneor more measurements of a patient's physiological condition that may bedetermined using a photoplethysmograph (PPG) signal.

In an embodiment, physiological parameters, such as one or more vitalsigns of a patient may be used to monitor effectiveness of painmanagement. For example, when a patient is sedated, anesthetized orotherwise provided pain medication, the patient may experience certainphysiological effects. Other physiological effects may be experiencedwhen the patient is subjected to pain. Accordingly, the effect of painor pain medication may cause the patient's physiological parameters,such as blood pressure, pulse rate, respiration rate, respirationeffort, or other parameter, alone or in combination, to change. Thesechanges to the patient's physiological parameters may be used as a basisfor determining whether a patient's pain is managed adequately. Theembodiments described herein may be applicable for a wide range of painmanagement scenarios, but may be of particular use during treatment ofan unconscious or sedated patient, a child, or other patient that maynot be capable of communicating a pain management need.

In an embodiment, blood pressure may be calculated using a PPG signalbased continuous non-invasive blood pressure (CNIBP) technique, furtherdescribed herein, using one or more sensors. In an embodiment,respiration rate and respiration effort may be calculated by analyzing aPPG signal obtained using a sensor, such as a pulse oximeter. Otherphysiological parameters may also be used in accordance with thedisclosure herein to provide comprehensive pain management monitoring.

In general, a change in one or more physiological parameters, such asblood pressure, respiration rate, respiration effort, or other parametermay provide an indication of effectiveness of pain management. Forexample, an increase in blood pressure for a sedated patient mayindicate that a patient is experiencing pain. Typically, receipt of painmedication may result in vasodilation or a reduction in blood pressure,or both. Such effect can be identified by blood pressure monitoring.Similarly, a change in pulse rate, respiration rate or respirationeffort, alone, or in combination with a change in blood pressure, mayindicate a problem with pain management.

In an embodiment, a change in one or more physiological parameters maybe used to identify a pain management problem that may require a remedy.In another embodiment, one or more physiological parameters may bemonitored after pain medication or sedation is administered to determineeffectiveness of a pain treatment. In another embodiment, a determinedeffectiveness of pain treatment may be used as a basis for administeringfuture pain treatments. These embodiments are described in furtherdetail herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative pulse oximetry system in accordance with anembodiment;

FIG. 2 is a block diagram of the illustrative pulse oximetry system ofFIG. 1 coupled to a patient in accordance with an embodiment;

FIGS. 3( a) and 3(b) show illustrative views of a scalogram derived froma PPG signal in accordance with an embodiment;

FIG. 3( c) shows an illustrative scalogram derived from a signalcontaining two pertinent components in accordance with an embodiment;

FIG. 3( d) shows an illustrative schematic of signals associated with aridge in FIG. 3( c) and illustrative schematics of a further waveletdecomposition of these newly derived signals in accordance with anembodiment;

FIGS. 3( e) and 3(f) are flow charts of illustrative steps involved inperforming an inverse continuous wavelet transform in accordance withembodiments;

FIG. 4 is a block diagram of an illustrative continuous waveletprocessing system in accordance with some embodiments;

FIGS. 5-7 are flowcharts of methods for monitoring pain management inaccordance with some embodiments;

FIG. 8 is a flowchart of a method for determining effectiveness of painmanagement in accordance with some embodiments.

FIG. 9 is a diagram of illustrative blood pressure measurements inaccordance with some embodiments; and

FIG. 10 is an illustrative PPG signal and scalogram in accordance withsome embodiments.

DETAILED DESCRIPTION

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient) and changes in blood volume in the skin.Ancillary to the blood oxygen saturation measurement, pulse oximetersmay also be used to measure the pulse rate of the patient. Pulseoximeters typically measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

The light passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent in the tissue and the related lightabsorption. Red and infrared wavelengths may be used because it has beenobserved that highly oxygenated blood will absorb relatively less redlight and more infrared light than blood with a lower oxygen saturation.By comparing the intensities of two wavelengths at different points inthe pulse cycle, it is possible to estimate the blood oxygen saturationof hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased on Lambert-Beer's law. The following notation will be used herein:

I(λ,t)=I _(o)(λ)exp(−(β_(o)(λ)+(1−s)β_(r)(λ))l(t))  (1)

where:λ=wavelength;t=time;I=intensity of light detected;I_(o)=intensity of light transmitted;s=oxygen saturation;β_(o), β_(r)=empirically derived absorption coefficients; andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., red and infrared (IR)), and then calculates saturation by solvingfor the “ratio of ratios” as follows.

1. First, the natural logarithm of (1) is taken (“log” will be used torepresent the natural logarithm) for IR and Red

log I=log I _(o)−(sβ _(o)+(1−s)β_(r))l  (2)

2. (2) is then differentiated with respect to time

$\begin{matrix}{\frac{{\log}\; I}{t} = {{- \left( {{s\; \beta_{o}} + {\left( {1 - s} \right)\beta_{r}}} \right)}\frac{l}{t}}} & (3)\end{matrix}$

3. Red (3) is divided by IR (3)

$\begin{matrix}{\frac{{\log}\; {{I\left( \lambda_{R} \right)}/{t}}}{{\log}\; {{I\left( \lambda_{IR} \right)}/{t}}} = \frac{{s\; {\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\; {\beta_{o}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}}} & (4)\end{matrix}$

4. Solving for s

$s = \frac{{\frac{{\log}\; {I\left( \lambda_{{IR}\;} \right)}}{t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}{\beta_{r}\left( \lambda_{IR} \right)}}}{\begin{matrix}{{\frac{{\; \log}\; {I\left( \lambda_{R} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} -} \\{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}$

Note in discrete time

$\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \; {I\left( {\lambda,t_{2}} \right)}} - {\log \; {I\left( {\lambda,t_{1}} \right)}}}$

Using log A−log B=log A/B,

$\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {\log \left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}$

So, (4) can be rewritten as

$\begin{matrix}{{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\log \left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log \left( \frac{I\left( {t_{1},\lambda_{IR}} \right)}{I\left( {t_{2},\lambda_{IR}} \right)} \right)}} = R} & (5)\end{matrix}$

where R represents the “ratio of ratios.” Solving (4) for s using (5)gives

$s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\; {\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}$

From (5), R can be calculated using two points (e.g., PPG maximum andminimum), or a family of points. One method using a family of pointsuses a modified version of (5). Using the relationship

$\begin{matrix}{\frac{{\log}\; I}{t} = \frac{{I}/{t}}{I}} & (6)\end{matrix}$

now (5) becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}}{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{IR}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{R}} \right)}}} \\{= R}\end{matrix} & (7)\end{matrix}$

which defines a cluster of points whose slope of y versus x will give Rwhere

x(t)=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁ ,λ _(R))

y(t)=[I(t ₂,λ_(R))−I(t ₁,λ_(IR))]I(t ₁,λ_(IR))

y(t)=Rx(t)  (8)

FIG. 1 is a perspective view of an embodiment of a pulse oximetry system10. System 10 may include a sensor 12 and a pulse oximetry monitor 14.Sensor 12 may include an emitter 16 for emitting light at two or morewavelengths into a patient's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the patient's tissue after passing through the tissue.

According to another embodiment and as will be described, system 10 mayinclude a plurality of sensors forming a sensor array in lieu of singlesensor 12. Each of the sensors of the sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of the array may be charged coupled device (CCD) sensor. Inanother embodiment, the sensor array may be made up of a combination ofCMOS and CCD sensors. The CCD sensor may comprise a photoactive regionand a transmission region for receiving and transmitting data whereasthe CMOS sensor may be made up of an integrated circuit having an arrayof pixel sensors. Each pixel may have a photodetector and an activeamplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, emitter 16 and detector 18 may be arrangedso that light from emitter 16 penetrates the tissue and is reflected bythe tissue into detector 18, such as a sensor designed to obtain pulseoximetry data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters based at least in parton data received from sensor 12 relating to light emission anddetection. In an alternative embodiment, the calculations may beperformed on the monitoring device itself and the result of the oximetryreading may be passed to monitor 14, Further, monitor 14 may include adisplay 20 configured to display the physiological parameters or otherinformation about the system. In the embodiment shown, monitor 14 mayalso include a speaker 22 to provide an audible sound that may be usedin various other embodiments, such as for example, sounding an audiblealarm in the event that a patient's physiological parameters are notwithin a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, pulse oximetry system 10 may also includea multi-parameter patient monitor 26. The monitor may be cathode raytube type, a flat panel display (as shown) such as a liquid crystaldisplay (LCD) or a plasma display, or any other type of monitor nowknown or later developed. Multi-parameter patient monitor 26 may beconfigured to calculate physiological parameters and to provide adisplay 28 for information from monitor 14 and from other medicalmonitoring devices or systems (not shown). For example, multiparameterpatient monitor 26 may be configured to display an estimate of apatient's blood oxygen saturation generated by pulse oximetry monitor 14(referred to as an “SpO₂” measurement), pulse rate information frommonitor 14 and blood pressure from a blood pressure monitor (not shown)on display 28.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

Calibration device 80, which may be powered by monitor 14, a battery, orby a conventional power source such as a wall outlet, may include anysuitable blood pressure calibration device. For example, calibrationdevice 80 may take the form of any invasive or non-invasive bloodpressure monitoring or measuring system used to generate reference bloodpressure measurements for use in calibrating the CNIBP monitoringtechniques described herein. Such calibration devices may include, forexample, an aneroid or mercury spygmomanometer and occluding cuff, apressure sensor inserted directly into a suitable artery of a patient,or any other device or mechanism used to sense, measure, determine, orderive a reference blood pressure measurement. In some embodiments,calibration device 80 may include a manual input device (not shown) usedby an operator to manually input reference or baseline blood pressuremeasurements obtained from some other source (e.g., an external invasiveor non-invasive blood pressure measurement system). Calibration device80 may also be used to provide reference or baseline measurements forrespiration rate, respiration effort, or other physiological parameters.

In accordance with some embodiments, the reference blood pressure,respiration rate, respiration effort, or other measurements may be usedto generate empirical data for one or multiple patients. In particular,the reference measurements may be used to provide coefficientinformation for the equations generated based on the empirical data thatmay be used to determine physiological parameter measurement using oneor more techniques based on a PPG signal.

Calibration device 80 may also access reference blood pressure,respiration rate, respiration effort, or other measurements stored inmemory (e.g., RAM, ROM, or a storage device). For example, in someembodiments, calibration device 80 may access reference measurementsfrom a relational database stored within calibration device 80, monitor14, or multi-parameter patient monitor 26. As described in more detailbelow, the reference measurements generated or accessed by calibrationdevice 80 may be updated in real-time, resulting in a continuous sourceof reference measurements for use in continuous or periodic calibration,as well as for providing baseline references for certain calculations.Alternatively, reference measurements generated or accessed bycalibration device 80 may be updated periodically, and calibration maybe performed on the same periodic cycle. In the depicted embodiments,calibration device 80 is connected to monitor 14 via cable 82. In otherembodiments, calibration device 80 may be a stand-alone device that maybe in wireless communication with monitor 14. Reference measurements maythen be wirelessly transmitted to monitor 14 for use in calibration. Instill other embodiments, calibration device 80 is completely integratedwithin monitor 14.

FIG. 2 is a block diagram of a pulse oximetry system, such as pulseoximetry system 10 of FIG. 1, which may be coupled to a patient 40 inaccordance with an embodiment. Certain illustrative components of sensor12 and monitor 14 are illustrated in FIG. 2. Sensor 12 may includeemitter 16, detector 18, and encoder 42. In the embodiment shown,emitter 16 may be configured to emit at least two wavelengths of light(e.g., RED and IR) into a patient's tissue 40. Hence, emitter 16 mayinclude a RED light emitting light source such as RED light emittingdiode (LED) 44 and an IR light emitting light source such as IR LED 46for emitting light into the patient's tissue 40 at the wavelengths usedto calculate the patient's physiological parameters. In one embodiment,the RED wavelength may be between about 600 nm and about 700 nm, and theIR wavelength may be between about 800 nm and about 1000 nm. Inembodiments where a sensor array is used in place of single sensor, eachsensor may be configured to emit a single wavelength. For example, afirst sensor emits only a RED light while a second only emits an IRlight.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the RED and IR wavelengths. Alternatively, each sensor inthe array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe RED and IR wavelengths in the patient's tissue 40.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelengths of lightemitted by emitter 16. This information may be used by monitor 14 toselect appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms. In addition, encoder 42 may include baseline or referenceinformation for certain physiological parameters that may not bepatient-specific. Encoder 42 may, for instance, be a coded resistorwhich stores values corresponding to the type of sensor 12 or the typeof each sensor in the sensor array, the wavelengths of light emitted byemitter 16 on each sensor of the sensor array, and/or the patient'scharacteristics. In another embodiment, encoder 42 may include a memoryon which one or more of the following information may be stored forcommunication to monitor 14: the type of the sensor 12; the wavelengthsof light emitted by emitter 16; the particular wavelength each sensor inthe sensor array is monitoring; a signal threshold for each sensor inthe sensor array; any other suitable information; or any combinationthereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the RED LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂ and pulse rate, using variousalgorithms and/or look-up tables based on the value of the receivedsignals and/or data corresponding to the light received by detector 18.Signals corresponding to information about patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from encoder 42 to a decoder 74. These signalsmay include, for example, encoded information relating to patientcharacteristics. Decoder 74 may translate these signals to enable themicroprocessor to determine the thresholds based on algorithms orlook-up tables stored in ROM 52. User inputs 56 may be used to enterinformation about the patient, such as age, weight, height, diagnosis,medications, treatments, pain management information, referenceinformation for physiological parameters, and so forth. In anembodiment, display 20 may exhibit a list of values which may generallyapply to the patient, such as, for example, age ranges or medicationfamilies, which the user may select using user inputs 56.

The optical signal through the tissue can be degraded by noise, amongother sources. One source of noise is ambient light that reaches thelight detector. Another source of noise is electromagnetic coupling fromother electronic instruments. Movement of the patient also introducesnoise and affects the signal. For example, the contact between thedetector and the skin, or the emitter and the skin, can be temporarilydisrupted when movement causes either to move away from the skin. Inaddition, because blood is a fluid, it responds differently than thesurrounding tissue to inertial effects, thus resulting in momentarychanges in volume at the point to which the oximeter probe is attached.

Noise (e.g., from patient movement) can degrade a pulse oximetry signalrelied upon by a physician, without the physician's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the doctor is watching the instrument orother parts of the patient, and not the sensor site. Processing pulseoximetry (i.e., PPG) signals may involve operations that reduce theamount of noise present in the signals or otherwise identify noisecomponents in order to prevent them from affecting measurements ofphysiological parameters derived from the PPG signals.

It will be understood that the present disclosure is applicable to anysuitable signals and that PPG signals are used merely for illustrativepurposes. Those skilled in the art will recognize that the presentdisclosure has wide applicability to other signals including, but notlimited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

Various approaches have been used for monitoring the blood pressure ofliving subjects. One approach is to insert a pressure sensor directlyinto a suitable artery of the subject. The sensor may be connected to asuitable monitoring device by a lead which passes through the subject'sskin. This approach may provide highly accurate and instantaneous bloodpressure measurements, but is very invasive. A surgical procedure isgenerally required to introduce the pressure sensor, and the fistulathrough which the lead exits the subject's body can provide a pathwayfor infection.

Another approach to measuring blood pressure uses a sphygmomanometer. Atypical sphygmomanometer has an occluding cuff capable of being wrappedaround a subject's arm. A pump is used to inflate the cuff, and ananeroid or mercury gravity sphygmomanometer is used to measure thepressure in the cuff. Such devices are widely used in hospitals, but arenot well adapted for providing continuous blood pressure monitoring.

Some continuous non-invasive blood pressure monitoring (CNIBP)techniques have been developed that involve the use of two probes orsensors positioned at two different locations on a subject's body. Theelapsed time, T, between the arrival of corresponding points of a pulsesignal at the two locations may then be determined using the two probesor sensors. The estimated blood pressure, p, may then be related to theelapsed time, T, by

p=a+b·ln(T)  (9)

where a and b are constants that are dependent upon the nature of thesubject and the signal detecting devices. Other blood pressure equationsusing elapsed time may also be used. These techniques may be referred toas differential pulse transit time (DPTT) based CNIBP.

In some embodiments, the constants a and b in equation (9) may bedetermined by performing a calibration. The calibration may involvetaking a reference blood pressure reading to obtain a reference bloodpressure P₀, measuring the elapsed time T₀ corresponding to thereference blood pressure, and then determining values for both of theconstants a and b from the reference blood pressure and elapsed timemeasurement. Calibration may be performed at any suitable time (e.g.,once initially after monitoring begins) or on any suitable schedule(e.g., a periodic or event-driven schedule).

The calibration may include performing calculations mathematicallyequivalent to

$\begin{matrix}{{a = {c_{1} + \frac{c_{2}\left( {P_{0} - c_{1}} \right)}{{\ln \left( T_{0} \right)} + c_{2}}}}{and}} & (10) \\{b = \frac{P_{0} - c_{1}}{{\ln \left( T_{0} \right)} + c_{2}}} & (11)\end{matrix}$

to obtain values for the constants a and b, where c₁ and c₂ arepredetermined constants.

In other embodiments, determining the plurality of constant parametersin the multi-parameter equation (1) may include performing calculationsmathematically equivalent to

a=P ₀−(c ₃ T ₀ +c ₄)ln(T ₀)  (12)

and

b=c ₃ T ₀ +c ₄  (13)

where a and b are first and second parameters and c₃ and c₄ arepredetermined constants.

In some embodiments, the multi-parameter equation (9) includes anon-linear function which is monotonically decreasing and concave upwardin a manner specified by the constant parameters.

Continuous and non-invasive blood pressure monitoring using thesetechniques is described in Chen et al. U.S. Pat. No. 6,566,251, which ishereby incorporated by reference herein in its entirety. The techniquedescribed by Chen et al. may use two sensors (e.g., ultrasound orphotoelectric pulse wave sensors) positioned at any two locations on asubject's body where pulse signals are readily detected. For example,sensors may be positioned on an earlobe and a finger, an earlobe and atoe, or a finger and a toe of a patient's body.

The use of multiple probes or sensors in non-invasive continuous bloodpressure monitoring provides reliable results. However, in someinstances, the use of multiple separate probes or sensors at differentlocations on the subject's body may be cumbersome, especially for amobile subject. Moreover, one of the multiple probes or sensors maybecome detached from the subject, resulting in a disruption in thecontinuous monitoring of the patent's blood pressure. Accordingly, sometechniques for continuously monitoring a subject's blood pressure useonly a single probe or sensor. In some embodiments, the single probe orsensor may detect a photoplethysmograph (PPG) signal generated, forexample, by a pulse oximeter. The PPG signal may then be analyzed andused to compute a time difference between two or more characteristicpoints in the PPG signal. From this time difference, reliable andaccurate blood pressure measurements may be computed on a continuous orperiodic basis. This technique is described in more detail in U.S.patent application Ser. No. 12/242,238 (Attorney Docket No. H-RM-01205(COV-11)), filed Sep. 30, 2008, entitled “SYSTEMS AND METHODS FORNON-INVASIVE BLOOD PRESSURE MONITORING,” which is incorporated byreference herein in its entirety. In some embodiments, blood pressuremeasurements may be determined based on pulses in a PPG signal detectedby a single sensor, for example, by measuring the area under a pulse ora portion of the pulse in the PPG signal. This technique is described inmore detail in U.S. patent application Ser. No. 12/242,867 AttorneyDocket No. H-RM-01206 (COV-13)), filed Sep. 30, 2008, entitled “SYSTEMSAND METHODS FOR NON-INVASIVE CONTINUOUS BLOOD PRESSURE DETERMINATION,”which is incorporated by reference herein in its entirety.

In one embodiment, a PPG signal may be transformed using a continuouswavelet transform. Information derived from the transform of the PPGsignal (i.e., in wavelet space) may be used to provide measurements ofone or more physiological parameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}{t}}}}} & (9)\end{matrix}$

where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (9) may be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation. Wavelets arecomposed of a range of frequencies, one of which may be denoted as thecharacteristic frequency of the wavelet, where the characteristicfrequency associated with the wavelet is inversely proportional to thescale a. One example of a characteristic frequency is the dominantfrequency. Each scale of a particular wavelet may have a differentcharacteristic frequency. The underlying mathematical detail requiredfor the implementation within a time-scale can be found, for example, inPaul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor &Francis Group 2002), which is hereby incorporated by reference herein inits entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms,Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined as

S(a,b)=|T(a,b)|²  (10)

where ‘∥’ is the modulus operator. The scalogram may be resealed foruseful purposes. One common resealing is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & (11)\end{matrix}$

and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane, Any reasonable definition of a ridge may beemployed in the method. Also included as a definition of a ridge hereinare paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the planeare labeled a “maxima ridge”.

For implementations requiring fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and then multiplying the resultby √{square root over (a)}.

In the discussion of the technology which follows herein, the“scalogram” may be taken to include all suitable forms of resealingincluding, but not limited to, the original unsealed waveletrepresentation, linear resealing, any power of the modulus of thewavelet transform, or any other suitable resealing. In addition, forpurposes of clarity and conciseness, the term “scalogram” shall be takento mean the wavelet transform, T(a,b) itself, or any part thereof. Forexample, the real part of the wavelet transform, the imaginary part ofthe wavelet transform, the phase of the wavelet transform, any othersuitable part of the wavelet transform, or any combination thereof isintended to be conveyed by the term “scalogram”.

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & (12)\end{matrix}$

where f_(c), the characteristic frequency of the mother wavelet (i.e.,at a=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Morletwavelet, is defined as:

ψ(t)=π^(−1/4)(e ^(i2πf) ⁰ ^(t) −e ^(−(2πf) ⁰ ⁾ ² ^(/2))e ^(−t) ²^(/2)  (13)

where f₀ is the central frequency of the mother wavelet. The second termin the parenthesis is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi (t)} = {\frac{1}{\pi^{1/4}}^{\; 2\pi \; f_{0}t}^{{- t^{2}}/2}}} & (14)\end{matrix}$

This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof equation (14) is not strictly a wavelet as it has a non-zero mean(i.e., the zero frequency term of its corresponding energy spectrum isnon-zero). However, it will be recognized by those skilled in the artthat equation (14) may be used in practice with f₀>>0 with minimal errorand is included (as well as other similar near wavelet functions) in thedefinition of a wavelet herein. A more detailed overview of theunderlying wavelet theory, including the definition of a waveletfunction, can be found in the general literature. Discussed herein ishow wavelet transform features may be extracted from the waveletdecomposition of signals. For example, wavelet decomposition of PPGsignals may be used to provide clinically useful information within amedical device.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a resealed wavelet space. For example, the pulsecomponent of a PPG signal produces a dominant band in wavelet space ator around the pulse frequency. FIGS. 3( a) and (b) show two views of anillustrative scalogram derived from a PPG signal, according to anembodiment. The figures show an example of the band caused by the pulsecomponent in such a signal. The pulse band is located between the dashedlines in the plot of FIG. 3( a). The band is formed from a series ofdominant coalescing features across the scalogram. This can be clearlyseen as a raised band across the transform surface in FIG. 3( b) locatedwithin the region of scales indicated by the arrow in the plot(corresponding to 60 beats per minute). The maxima of this band withrespect to scale is the ridge. The locus of the ridge is shown as ablack curve on top of the band in FIG. 3( b). By employing a suitableresealing of the scalogram, such as that given in equation (11), theridges found in wavelet space may be related to the instantaneousfrequency of the signal. In this way, the pulse rate may be obtainedfrom the PPG signal. Instead of resealing the scalogram, a suitablepredefined relationship between the scale obtained from the ridge on thewavelet surface and the actual pulse rate may also be used to determinethe pulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a resealed wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary; varying in scale, amplitude, or both over time.FIG. 3( c) shows an illustrative schematic of a wavelet transform of asignal containing two pertinent components leading to two bands in thetransform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In this embodiment, the band ridge is defined as the locus ofthe peak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. This will be referred to as the “primary band”.In addition, it may be assumed that the system from which the signaloriginates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band Bthen the information within band B can become ambiguous (i.e., obscured,fragmented or missing). In this case, the ridge of band A may befollowed in wavelet space and extracted either as an amplitude signal ora scale signal which will be referred to as the “ridge amplitudeperturbation” (RAP) signal and the “ridge scale perturbation” (RSP)signal, respectively. The RAP and RSP signals may be extracted byprojecting the ridge onto the time-amplitude or time-scale planes,respectively. The top plots of FIG. 3( d) show a schematic of the RAPand RSP signals associated with ridge A in FIG. 3( c). Below these RAPand RSP signals are schematics of a further wavelet decomposition ofthese newly derived signals. This secondary wavelet decomposition allowsfor information in the region of band B in FIG. 3( c) to be madeavailable as band C and band D. The ridges of bands C and D may serve asinstantaneous time-scale characteristic measures of the signalcomponents causing bands C and D. This technique, which will be referredto herein as secondary wavelet feature decoupling (SWFD), may allowinformation concerning the nature of the signal components associatedwith the underlying physical process causing the primary band B (FIG.3(c)) to be extracted when band B itself is obscured in the presence ofnoise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts. In one embodiment, there is an inversecontinuous wavelet transform which allows the original signal to berecovered from its wavelet transform by integrating over all scales andlocations, a and b:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi \left( \frac{t - b}{a} \right)}\frac{{a}{b}}{a^{2}}}}}}} & (15)\end{matrix}$

which may also be written as:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi_{a,b}(t)}\frac{{a}{b}}{a^{2}}}}}}} & (16)\end{matrix}$

where C_(g) is a scalar value known as the admissibility constant. It iswavelet type dependent and may be calculated from:

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{{{\hat{\psi}(f)}}^{2}}{f}{f}}}} & (17)\end{matrix}$

FIG. 3( e) is a flow chart of illustrative steps that may be taken toperform an inverse continuous wavelet transform in accordance with theabove discussion. An approximation to the inverse transform may be madeby considering equation (15) to be a series of convolutions acrossscales. It shall be understood that there is no complex conjugate here,unlike for the cross correlations of the forward transform. As well asintegrating over all of a and b for each time t, this equation may alsotake advantage of the convolution theorem which allows the inversewavelet transform to be executed using a series of multiplications. FIG.3( f) is a flow chart of illustrative steps that may be taken to performan approximation of an inverse continuous wavelet transform. It will beunderstood that any other suitable technique for performing an inversecontinuous wavelet transform may be used in accordance with the presentdisclosure.

The techniques described above, in particular, wavelet transformation,and scalogram analysis, may be used to determine respiration rate andrespiration effort. In an embodiment, respiration effort may be found torelate to a measure of strength of at least one repetitive feature in aPPG signal. In another embodiment, effort may relate to physical effortof a process that may affect the signal (e.g. effort may relate to workof a process). Effort may also be determined by analyzing the signalrepresentation. For example, changes in effort induce or change variousfeatures of the signal used to generate the scalogram. For example, theact of breathing may cause a breathing band to become present in ascalogram that was derived from a PPG signal. The breathing band mayoccur at or about a scale having a characteristic frequency thatcorresponds to the breathing frequency (respiration rate). Any featureswithin this band or other bands on the scalogram (e.g., energy,amplitude, phase, or modulation) may result from changes in breathingand/or breathing effort and which may be correlated with the patient'sbreathing effort. Specific techniques for analyzing scalogram featuresfor respiration effort are described in more detail in U.S. patentapplication Ser. No. 12/245,366 (Attorney Docket No. H-RM-01194(COV-4)), filed Oct. 3, 2008, entitled “SYSTEMS AND METHODS FORDETERMINING EFFORT,” the entire contents of which are incorporated byreference.

FIG. 4 is an illustrative continuous wavelet processing system inaccordance with an embodiment. In this embodiment, input signalgenerator 410 generates an input signal 416. As illustrated, inputsignal generator 410 may include oximeter 420 coupled to sensor 418,which may provide as input signal 416, a PPG signal. It will beunderstood that input signal generator 410 may include any suitablesignal source, signal generating data, signal generating equipment, orany combination thereof to produce signal 416. Signal 416 may be anysuitable signal or signals, such as, for example, biosignals (e.g.,electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal), dynamic signals, non-destructive testingsignals, condition monitoring signals, fluid signals, geophysicalsignals, astronomical signals, electrical signals, financial signalsincluding financial indices, sound and speech signals, chemical signals,meteorological signals including climate signals, and/or any othersuitable signal, and/or any combination thereof.

In this embodiment, signal 416 may be coupled to processor 412.Processor 412 may be any suitable software, firmware, and/or hardware,and/or combinations thereof for processing signal 416. For example,processor 412 may include one or more hardware processors (e.g.,integrated circuits), one or more software modules, computer-readablemedia such as memory, firmware, or any combination thereof. Processor412 may, for example, be a computer or may be one or more chips (i.e.,integrated circuits). Processor 412 may perform the calculationsassociated with the continuous wavelet transforms of the presentdisclosure as well as the calculations associated with any suitableinterrogations of the transforms. Processor 412 may perform any suitablesignal processing of signal 416 to filter signal 416, such as anysuitable band-pass filtering, adaptive filtering, closed-loop filtering,and/or any other suitable filtering, and/or any combination thereof.

Processor 412 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 412to, for example, store data corresponding to a continuous wavelettransform of input signal 416, such as data representing a scalogram. Inone embodiment, data representing a scalogram may be stored in RAM ormemory internal to processor 412 as any suitable three-dimensional datastructure such as a three-dimensional array that represents thescalogram as energy levels in a time-scale plane. Any other suitabledata structure may be used to store data representing a scalogram.

An optional pain management controller component 413 maybe coupled toprocessor 412 and output 414. The pain management controller 413 isconfigured to provide pain management specific signal processing as wellas issuing a control signal to output 414. In an embodiment, such signalprocessing could also be provided by processor 412. Pain managementspecific signal processing may include processing of signals relating topain management data, PPG signal information, such as input signal 416,as well as processing information relating to reference measurements orranges for physiological parameters. Pain management controller 413 mayalso be coupled to a medication dispensation component (not shown), towhich the pain management controller 413 may transmit a control signalindicating that pain medication may be required, increased, decreased,or other signal.

Pain management controller 413 and processor 412 may be coupled tooutput 414. Output 414 may be any suitable output device such as, forexample, one or more medical devices (e.g., a medical monitor thatdisplays various physiological parameters, a medical alarm, or any othersuitable medical device that either displays physiological parameters oruses the output of processor 412 as an input), one or more displaydevices (e.g., monitor, PDA, mobile phone, any other suitable displaydevice, or any combination thereof), one or more audio devices, one ormore memory devices (e.g., hard disk drive, flash memory, RAM, opticaldisk, any other suitable memory device, or any combination thereof), oneor more printing devices, any other suitable output device, or anycombination thereof.

In some embodiments, for example, in order to determine respirationeffort, processor 412 may first transform the signal into any suitabledomain, for example, a Fourier, wavelet, spectral, scale, time,time-spectral, time-scale domains, or any transform space. Processor 412may further transform the original and/or transformed signals into anyof the suitable domains as necessary. Processor 412 may represent theoriginal or transformed signals in any suitable way, for example,through a two-dimensional representation or three-dimensionalrepresentation, such as a spectrogram or scalogram.

After processor 412 represents the signals in a suitable fashion,processor 412 may then find and analyze selected features in the signalrepresentation of signal 416 to determine effort. Selected features mayinclude the value, weighted value, or change in values with regard toenergy, amplitude, frequency modulation, amplitude modulation, scalemodulation, differences between features (e.g., distances between ridgeamplitude peaks within a time-scale band).

For example, selected features may include features in a time-scale bandin wavelet space or a resealed wavelet space described above. As anillustrative example, the amplitude or energy of the band may beindicative of the breathing effort of a patient when the band is thepatient's breathing band. Furthermore, changes in the amplitude orenergy of the band may be indicative of a change in breathing effort ofa patient. Other time-scale bands may also provide informationindicative of breathing effort, For example, amplitude modulation, orscale modulation of a patient's pulse band may also be indicative ofbreathing effort. Effort may be correlated with any of the aboveselected features, other suitable features, or any combination thereof.

The selected features may be localized, repetitive, or continuous withinone or more regions of the suitable domain space representation ofsignal 416. The selected features may not necessarily be localized in aband, but may potentially be present in any region within a signalrepresentation. For example, the selected features may be localized,repetitive, or continuous in scale or time within a wavelet transformsurface. A region of a particular size and shape may be used to analyzeselected features in the domain space representation of signal 416. Theregion's size and shape may be selected based at least in part on theparticular feature to be analyzed. As an illustrative example, in orderto analyze a patient's breathing band for one or more selected features,the region may be selected to have an upper and lower scale value in thetime-scale domain such that the region covers a portion of the band, theentire band, or the entire band plus additional portions of thetime-scale domain. The region may also have a selected time windowwidth.

The bounds of the region may be selected based at least in part onexpected locations of the features. For example, the expected locationsmay be based at least in part on empirical data of a plurality ofpatients. The region may also be selected based at least in part onpatient classification. For example, an adult's breathing band locationgenerally differs from the location of a neonatal patient's breathingband. Thus, the region selected for an adult may be different than theregion selected for a neonate.

In some embodiments, the region may be selected based at least in parton features within a scalogram. For example, the scalogram for a patientmay be analyzed to determine the location of the breathing band and itscorresponding ridge. The breathing band ridge may be located usingstandard ridge detection techniques. Ridges may also be detected usingthe techniques described in Watson et al., U.S. application Ser. No.12/245,326 (Attorney Docket No. H-RM-01197 (COV-2)), filed Sep. 30,2008, entitled “Systems and Method for Ridge Selection in Scalograms ofSignals,” which is incorporated by reference herein in its entirety. Asan illustrative example, if the ridge of a band were found to be atlocation X, the region may be selected to extend a predetermineddistance above and below location X. Alternatively, the band itself maybe analyzed to determine its size. The upper and lower bounds of theband may be determined using one or more predetermined or adaptivethreshold values. For example, the upper and lower bounds of the bandmay be determined to be the location where the band crosses below athreshold. The width of the region may be a predetermined amount of timeor it may vary based at least in part on the characteristics of theoriginal signal or the scalogram. For example, if noise is detected, thewidth of the region may be increased or portions of the region may beignored.

In some embodiments, the region may be determined based at least in parton the repetitive nature of the selected features. For example, a bandmay have a periodic feature. The period of the feature may be used todetermine bounds of the region in time and/or scale.

The size, shape, and location of the one or more regions may also beadaptively manipulated using signal analysis. The adaptation may bebased at least in part on changing characteristics of the signal orfeatures within the various domain spaces.

As a signal is being processed, for example by processor 412 the regionmay be moved over the signal in any suitable domain space over anysuitable parameter in order to determine the value or change in value ofthe selected features. The processing may be performed in real-time orvia a previously recorded signal. For example, a region may move overthe breathing band in the time-scale domain over time. When the selectedfeatures have been analyzed, they may be correlated with effort overtime, and hence show the value or change in value of effort over time.

In some embodiments, the determined effort may be provided as aquantitative or qualitative value indicative of effort, The quantitativeor qualitative value may be determined using the value or change invalues in one or more suitable metrics of relevant information, such asthe selected features mentioned above. The quantitative or qualitativevalues may be based on an absolute difference from a reference or acalibrated value of the features. For example, breathing effort of apatient may be calibrated upon initial setup. Alternatively, the valuesmay be indicative of a relative change in the features such as thechange in distance between peaks in amplitude, changes in magnitude,changes in energy level, or changes in the modulation of features.

The quantitative or qualitative value of effort may be provided to bedisplayed on a display, for example on display 28. Effort may bedisplayed graphically on a display by depicting values or changes invalues of the determined effort or of the selected features describedabove. The graphical representation may be displayed in one, two, ormore dimensions and may be fixed or change with time. The graphicalrepresentation may be further enhanced by changes in color, pattern, orany other visual representation.

The depiction of effort through a graphical, quantitative, qualitativerepresentation, or combination of representations may be presented onoutput 414 and may be controlled by processor 412.

System 400 may be incorporated into system 10 (FIGS. 1 and 2) in which,for example, input signal generator 410 may be implemented as parts ofsensor 12 and monitor 14 and processor 412 may be implemented as part ofmonitor 14. Arrangements of systems 400, 10 (FIGS. 1 and 2) may be usedto provide a comprehensive pain management system as described herein.

In an embodiment, pain management monitoring may be provided using thesteps of the flowchart depicted in FIG. 5. As shown, a referencemeasurement may be received at step 500. In some embodiments, a first orreference measurement may be received from calibration device 80 (FIGS.1 and 2) based on reference information or other baseline data. In otherembodiments, a reference measurement may be manually input, for examplevia calibration device 80 (FIGS. 1 and 2) or a user input component 56(FIG. 2), encoder 42 (FIG. 1) or other suitable component. In otherembodiments, a reference measurement may be calculated using processor412 (FIG. 4) based on a signal 416 (FIG. 4) obtained using sensor 12 andmonitor 14 (FIGS. 1 and 2). Generally speaking, the referencemeasurement may be a single value or range of values for one or morephysiological parameters, such as blood pressure, respiration rate,respiration effort, pulse rate, or other parameter, that is suitable forproviding a reference or benchmark for monitoring a patient's painmanagement. The reference measurement may be patient-specific, or areference measurement that is appropriate for a similar cohort.

At step 510 a PPG signal may be detected. In an embodiment, the PPGsignal may be detected by sensor 12 (FIGS. 1 and 2) that may be placedon a patient being monitored. In an embodiment, sensor 12 (FIGS. 1 and2) is preferably a component that provides continuous readings, andwhich may be used to provide, for example, continuous non-invasive bloodpressure measurements, or other measurements on a continuous basis.

The detected PPG signal may be used to determine a physiologicalparameter of the patient. For example, at step 520, blood pressure, orother physiological parameter may be determined based on the PPG signal416 (FIG. 4) using, for example, processor 412 (FIG. 4). It will beunderstood that other types of physiological parameters, such asrespiration effort, pulse rate, respiration rate, or other parametercould be determined by processor 412 (FIG. 4) at step 520 and equally oradditionally applied to the methods for monitoring pain managementdiscussed herein.

In an illustrative example, a blood pressure measurement determined atstep 520 may be derived based on a signal 416 (FIG. 4) received from asensor 12 (FIGS. 1 and 2), which may be a CNIBP device, using any of thetechniques described herein, or a technique known in the art. Forexample, blood pressure may be calculated based on an elapsed time, T,between the arrival of corresponding points of a pulse signal at twolocations using the two sensors 12 (FIGS. 1 and 2). In the case that asingle sensor 12 (FIGS. 1 and 2) is used, the single probe or sensor maydetect a PPG signal which may then be analyzed and used to compute atime difference between two or more characteristic points in the PPGsignal. Analysis of the PPG signal and calculations of physiologicalparameters based on the PPG signal may be provided by processor 48 (FIG.2) or 412 (FIG. 4) or other processing component.

At step 530 a comparison of the reference measurement and the determinedphysiological parameter measurement may be performed by processor 48(FIG. 2) or 412 (FIG. 4). Generally speaking, for the comparison at step530 the reference measurement is the reference measurement received atstep 500 and the physiological parameter measurement is the measurementdetermined at step 520. In some embodiments, the comparison at step 530may be performed using two physiological parameter measurements taken atdifferent times while monitoring a patient. The comparison at step 530may include a straight comparison of reference and blood pressurevalues, or any combination of comparative techniques that may includecomparing an acceptable range of suitable baseline values. In addition,a comparison at step 530 may include comparisons of reference anddetermined values for more than one physiological parameter. Forexample, a comparison at step 530 may include comparing blood pressuremeasurements and respiration effort. Certain reference values may beused for such a comparison of a plurality of physiological parametersand may include varying acceptable ranges.

If the determined physiological measurement (or measurements) is foundto differ from the reference measurement, for example, exceed or be lessthan an acceptable range of values for the reference, a signal may begenerated at step 540. The signal generated at step 540 may be generatedby processor 48 (FIG. 2) or 412 (FIG. 4) for output to 414 (FIG. 4) andmay include an alarm that may be audible via speaker 22 (FIG. 1),displayed on monitor 28 (FIG. 1), or otherwise manifested. Other typesof signals generated at step 540 may include a signal that may indicatethat pain medication or other pain management treatment may be needed.Such a signal may be transmitted to a pain management controller 413(FIG. 4) that may be communicatively coupled to the processor 48 (FIG.2) or 412 (FIG. 4) that may automatically control dispensation of painmedication to a patient, or provide an indication to a medical providerthat pain medication is required.

In the event that the blood pressure management is determined to beequivalent to or below the reference measurement, or within anacceptable range of reference values, the system 10 (FIG. 1) or 400(FIG. 4) may continue monitoring the patient, at step 550. Continuingmonitoring of the patient may include determining subsequentphysiological parameter measurements and comparing the subsequentmeasurements to a reference measurement or other measurements toidentify any changes in physiological parameters that may indicate apain management problem. For example, continuous blood pressuremeasurements may be obtained using a CNIBP technique. The CNIBPmeasurements may be compared against a reference baseline or againstprior CNIBP values to identify changes in blood pressure that mayindicate a pain management problem.

Such continuous monitoring may also be implemented following the signalgeneration at step 540 as shown in FIG. 6. Turning to FIG. 6, a signalmay be generated at step 600 in a manner similar to that described inconnection with step 540, such as generating an audible or visiblealarm, or other signal. Such signal may be generated by a painmanagement controller 413 (FIG. 4) or processor 412 (FIG. 4) and sent tooutput 414 (FIG. 4). At step 610, monitoring of the patient may continueafter the signal generation, for example, by continuing to receivesignals 416 (FIG. 4) from a sensor 418 (FIG. 4) or 12 (FIGS. 1 and 2).The continued monitoring of the patient generally provides additionalPPG signal information that may be used to determine a second or otherfollowing physiological parameter measurement at step 620. Comparisonsof the second or following physiological parameter may be made againstearlier measurements and a reference measurement. As mentioned above,ranges of values for comparative reference may be used. In addition, oneor more physiological parameters and one or more periodic values of eachmay be compared in a continuing monitoring process to identify a changein the patient's physiological condition. The comparisons may be used todetermine an effectiveness of a patient's pain management routine atstep 630. For example, for a patient whose physiological parametermeasurements fall within a suitable range or that do not differ from areference measurement (for example, by more than a selected amount),pain management be determined to be effective, as further describedherein. In such a case, monitoring may continue at step 610. For apatient whose physiological parameter measurements differ from suitableranges or references may be determined to have a pain managementproblem, or that the pain management is not effective. In this case,additional measurements may be determined for confirmation purposes.Alternatively, or in addition, a signal may be generated to output 414(FIG. 4), again at step 600 such as an alarm or indication that painmanagement may be required. Effectiveness of ongoing pain managementtreatment may also be provided using continuing monitoring methods asdescribed herein. For example, if a patient has been identified ashaving a pain management problem, monitoring may be undertaken afteradditional pain management treatments or medication is provided toensure that the additional measures are adequate or effective.

Such an approach is further described in connection with FIG. 7 whichdepicts a flowchart for monitoring pain management effectiveness. A PPGsignal may be detected at step 700. The PPG signal may be detected by asensor 418 (FIG. 4) or 12 (FIGS. 1 and 2) and used to determine aninitial physiological parameter measurement at step 710. As discussedpreviously, the physiological parameter may be determined using anytechnique described herein, or known in the art. For example,respiration rate and respiration effort may be determined by analyzingfeatures of a scalogram using processor 48 (FIG. 2) or 412 (FIG. 4)derived from the PPG signal 416 (FIG. 4). Respiration rate and effortmay be of particular importance in ascertaining effectiveness of a painmanagement routine because increased and decreased respiration rate andeffort may be indicative of a problem. Respiration rate and respirationeffort measurements are useful in combination with blood pressureinformation because a heavily sedated individual's blood pressure may benormal to low which would not indicate a pain management problem.However, a patient with normal to low blood pressure and highrespiration effort may have received excessive pain medication. Such ascenario could cause a signal to be transmitted to output 414 (FIG. 4)indicating that a reduction in pain medication or sedation would beappropriate.

A pain management controller 413 (FIG. 4) may receive information aboutpain management at step 720. Pain management information may includeinformation such as an indication that a pain treatment has beenadministered, an indication that pain treatment may be needed, or otherinformation. For example, pain management information may be informationindicating that a patient has recently received a sedative, or that thepatient has recently received a reduced pain medication dose. To ensurethat the sedative is an appropriate treatment, a second measurement ofthe physiological parameter(s) may be determined at step 730. The secondmeasurement may be determined using the technique used at step 710 orother technique. The second measurement may be compared against theinitial measurement determined at step 720 or a baseline reference todetermine whether the pain management treatment is effective at step740. In particular, if the patient's physiological parametermeasurements fall within an acceptable range or at or below a suitablebenchmark, a pain management treatment may be determined to beeffective. In this case, continued monitoring may be performed byreturning to step 710. In the event that the pain management treatmentis found not to be effective, a signal may be generated at step 750 andsent to output 414 (FIG. 4). The signal may be, for example, an alarm oran indication that pain management is not effective. Following thesignal generation at step 750, monitoring may continue at step 720.

In an embodiment, pain management effectiveness may be determinedfollowing the steps of the flowchart depicted in FIG. 8. For example, atstep 810, a first and second measurement may be compared. Typically, thefirst and second measurements may be obtained via calculations of a PPGsignal 416 (FIG. 4) detected by sensor 418 (FIG. 4) or 12 (FIGS. 1 and2), input as a reference value, or other measurement. As discussedpreviously, the measurements herein may be for any physiologicalparameter. For a pain management effectiveness determination, the firstand second measurements are typically the same type of physiologicalparameter. The comparison at step 810 may be performed by processor 412(FIG. 4) or other computing or processing component, and may include oneor more inquiries. In an embodiment, the processor 412 (FIG. 4)determines whether the second measurement differs from the firstmeasurement at step 815. For certain types of physiological parameters,such as respiration rate, problems may arise if the second measurementis less than the first measurement. In such a case, at step 815, thecomparison inquiry may be whether the second measurement is less thanthe first. Either approach may be used.

In addition to, or instead of, step 815 the processor 412 (FIG. 4) maydetermine whether the second measurement is within a range of expectedvalues at step 820. The expected values used in step 820 may beprovided, for example, via user inputs 56 (FIG. 1), calibration device80 (FIG. 1), pain management controller 413 (FIG. 4). The expectedvalues may be a range of suitable values for a measurement, or athreshold for an expected change in a first and second value. If thesecond measurement is not within the range of expected values, a signalis generated at step 825. The signal generated at step 825 may begenerated by processor 412 (FIG. 4) or pain management controller 413(FIG. 4) and communicated to output 414 (FIG. 4), a pain medicationdispenser component, or other component. The signal generated at step825 typically indicates that a problem with pain management may exist,and the signal may include, for example, a signal indicating a visibleor audible alarm. In other embodiments, the signal generated at step 825may be an indication that a different or additional medication dose maybe required. In some embodiments, the signal generated at step 825 maybe a control signal for a pain medication dispenser to administer a doseof medication. Generally following a signal generation at step 825,monitoring of a patient continues by repeating a measurement sample atstep 830. In a preferred embodiment, the measurements are repeated on acontinual basis, and comparisons may be repeated, for example at steps810 and 815.

If at step 820 the second measurement is determined by the processor 412(FIG. 4) or pain management controller 413 (FIG. 4) to be within anexpected range of values, additional information about a patient may beconsulted if it is available at step 835. Additional patient data mayinclude pain management treatment dosage history, prior measurements,additional data about a patient's pain management requirements, or otherpatient data. If no additional patient data is available, monitoringcontinues with repeated measurements, at step 830. If the additionalpatient data is available and within a certain expected range of valuesat step 840, monitoring will also continue with repeated measurements atstep 830. If however, the additional patient data is available and doesnot fall within an expected range of values, a signal may be generatedat step 825 indicating that a problem may exist. In an embodiment,determining effectiveness of pain management is a multi-facetedcalculation that can be adapted to one or more different types ofphysiological parameters, as well as to a plurality of treatments.

An illustrative example of the effect of pain and pain treatment onblood pressure is depicted in FIG. 9 which shows a patient's systolicblood pressure at the upper line 910 and diastolic blood pressure atlower line 920 over time during a surgical operation. The chart in FIG.9 was produced during a hip replacement surgery using a CNIBP monitoringdevice. Initial measurements for the patient's blood pressure aredetermined beginning at 8:19. The surgery start time was at point 930.As can be seen, the patient's blood pressure did not immediately changeupon commencement of the surgery. At point 940, epinephrine, a routinetreatment for anesthesia, was administered to the patient. At point 950,the patient's hip joint was removed from its socket, which caused thefollowing increase in blood pressure at point 960. The pain associatedwith the hip joint removal appears to have been fleeting as thepatient's blood pressure reduced to levels similar to those beforesurgery began. However, at point 970 cautery was undertaken causinganother increase in blood pressure. Following the blood pressureincrease at point 970, a sedative and other medication was administeredto the patient at point 980 resulting a decrease in the patient's bloodpressure. Pain relief and/or the drugs associated with pain relief maybe accompanied by vasodilation or a reduction in blood pressure or both,which effects may be picked up by a CNIBP monitor. As the surgerycontinued, additional increases in blood pressure were determined atpoints 990 and 995 that were used to provide a signal to administeradditional pain treatments to the patient. As can be seen from FIG. 9continuous monitoring of a patient's blood pressure may be used to gaugeeffectiveness of pain management. In addition, the continuous monitoringof a patient's blood pressure may be useful feedback forself-administered pain management treatments. For example, for a patientthat is self-administering pain medication, the blood pressure readingsmay be a useful reference to determine whether the pain medication issufficient, excessive, or insufficient. Similarly, other physiologicalparameters may also be used as a reference for self-administering paintreatments.

In another example, a shape or features of a scalogram derived from aPPG signal may be used to identify changes in physiological parametersthat may indicate a pain management problem. FIG. 10 depicts a scalogramderived from a PPG signal. The signal used in the scalogram shown inFIG. 10 is oriented so that the systolic peak is a maximum on the signalpulse, i.e., it is inverted from the incoming signal. This signalorientation is representative of absorption of the light. The originalorientation of the signal could also be used, in which case, thebaseline shifts would be in a direction opposite to the shifts shown inFIG. 10. The scalogram in FIG. 10 may be derived by processor 48 (FIG.2) or 412 (FIG. 4) based on a PPG signal detected by sensor 12 (FIGS. 1and 2). Certain features of the PPG signal and resulting scalogram maybe used to identify changes in a physiological condition. Such featuresmay include a shape or morphology, scale or amplitude of the signal. Forexample, as shown in FIG. 9 a high scale of the PPG signal appears attime 0 to point 1015 at which time an ice cube was placed on a patient.Following placement of the ice cube, the signal baseline lowers and theamplitude of the individual pulse signals reduces. The signal baselineshift and amplitude reduction may be indicative of vasoconstriction,which is an indication that pain relief may be needed. Shortly, afterthe ice cube is removed at point 1025 the signal amplitude increase andbaseline shift. The shift in the baseline characteristics of the PPG mayindicate that vasodilation is occurring. Monitoring a PPG signalbaseline shift may also be used for monitoring a patient's conditionfollowing administration of a pain relief treatment.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure. Thefollowing claims may also describe various aspects of this disclosure.

1. A method for monitoring pain management, comprising: receiving, at aprocessor, a first respiration rate measurement; generating, using aprocessor, a PPG waveform based at least in part on the PPG signal;analyzing, using the processor, one or more features within the PPGsignal; determining, using the processor, a second respiration ratemeasurement based at least in part on the one or more analyzed features;comparing, using the processor, the second respiration rate measurementand at least the first respiration rate measurement; and if the secondrespiration rate measurement differs from the first respiration ratemeasurement by a selected value, generating a signal.
 2. The method ofclaim 1 further comprising generating, using the processor, a scalogrambased at least in part on the PPG signal.
 3. The method of claim 1wherein the receiving the first respiration rate measurement comprisesreceiving an input indicating a respiration rate range, or receivingreference respiration rate information based on the PPG signal.
 4. Themethod of claim 1 wherein the signal indicates at least one of the groupof: an alarm, a request to provide pain management, and a request toprovide sedation.
 5. The method of claim 1, further comprising, if thesecond respiration rate measurement differs from the first respirationrate measurement by a selected value, automatically providing painmanagement or sedation by sending a signal to a pain managementcontroller indicating a request for pain management or sedation.
 6. Themethod of claim 5, further comprising: determining, using the processor,a third respiration rate measurement based at least in part on the PPGsignal; and determining, using the processor, an effectiveness of thepain management or sedation based at least in part on the secondrespiration rate measurement and the third respiration rate measurement.7. The method of claim 6, wherein the determining, using the processor,the effectiveness of the pain management or sedation is further based atleast in part on data indicating a blood pressure measurement or arespiration effort.
 8. A method for monitoring pain management,comprising: analyzing, using the processor, one or more features withinthe PPG signal; determining, using the processor, a first respirationrate measurement based at least in part on the one or more analyzedfeatures of the PPG signal; receiving information at the processorindicating that pain management or sedation has been administered to apatient; determining, using the processor, a second respiration ratemeasurement based at least in part on the PPG signal at a time after thepain management or sedation has been administered to a patient; anddetermining, using the processor, an effectiveness of the painmanagement or sedation based at least in part on a comparison of thefirst respiration rate measurement and the second respiration ratemeasurement.
 9. The method of claim 8 further comprising generating,using the processor, a scalogram based at least in part on the PPGsignal.
 10. The method of claim 8 further comprising, if the painmanagement or sedation is determined to not be effective: generating asignal comprising at least one of the group of: indicating an alarm,indicating a request to provide pain management, and indicating arequest to provide sedation.
 11. The method of claim 8, wherein thedetermining, using the processor, the effectiveness of the painmanagement or sedation is further based at least in part on dataindicating a blood pressure measurement or a respiration rate.
 12. Asystem for monitoring pain management, comprising: a sensor having atleast one emitter and at least one detector configured to detect a PPGsignal, the sensor coupled to a processor and control circuitryconfigured to: receive a first respiration rate measurement; analyze oneor more features within the PPG signal; determine a second respirationrate measurement based at least in part on the one or more analyzedfeatures within the PPG signal; compare the second respiration ratemeasurement and at least the first respiration rate measurement; and ifthe second respiration rate measurement differs from the firstrespiration rate measurement by a selected value, generate a signal. 13.The system of claim 12 wherein the received first respiration ratemeasurement comprises receiving an input indicating a respiration raterange, or receiving reference respiration rate information based atleast in part on the PPG signal.
 14. The system of claim 12 wherein thesignal indicates at least one of the group of: an alarm, a request toprovide pain management, and a request to provide sedation.
 15. Thesystem of claim 12, further comprising, if the second respiration ratemeasurement differs from the first respiration rate measurement by aselected value, the control circuitry is further configured toautomatically provide pain management or sedation by sending a signal toa pain management controller indicating a request for pain management orsedation.
 16. The system of claim 15, wherein the control circuitry isfurther configured to determine a third respiration rate measurementbased at least in part on the detected PPG signal; and determine aneffectiveness of the pain management or sedation based at least in parton the second respiration rate measurement and the third respirationrate measurement.
 17. The system of claim 16, wherein the determining,using the processor, the effectiveness of the pain management orsedation is further based at least in part on data indicating a bloodpressure measurement or a respiration rate.
 18. A system for monitoringpain management, comprising: a sensor having at least one emitter and atleast one detector configured to detect a PPG signal, the sensor coupledto a processor and control circuitry configured to: analyze one or morefeatures within the PPG signal; determine a first respiration ratemeasurement based at least in part on the one or more analyzed featuresof the PPG signal; receive information indicating that pain managementor sedation has been administered to a patient; determine a secondrespiration rate measurement based at least in part on the PPG signal ata time after the pain management or sedation has been administered to apatient; and determine an effectiveness of the pain management orsedation based at least in part on a comparison of the first respirationrate measurement and the second respiration rate measurement.
 19. Thesystem of claim 18 wherein the control circuitry is further configuredto generate, a scalogram based at least in part on the PPG signal. 20.The system of claim 18 wherein the control circuitry is furtherconfigured, if the pain management or sedation is determined to not beeffective, to generate a signal comprising at least one of the group of:indicating an alarm, indicating a request to provide pain management,and indicating a request to provide sedation based at least in part ondata indicating a blood pressure or a respiration effort.